Linganisha mbinu

	Case-Time-Control Design	Poisson Rate Regression
Nyanja	Social Epidemiology	Social Epidemiology
Familia	Process / pipeline	Regression model
Mwaka wa asili	1995	1983
Mwanzilishi	Samy Suissa; Sander Greenland	E. L. Frome (rate formulation); A. C. Cameron & P. K. Trivedi (modern count-data treatment)
Aina	Self-controlled observational design with a time-trend control series	Generalized linear model for event rates and counts with log link and person-time offset
Chanzo asilia	Suissa, S. (1995). The case-time-control design. Epidemiology, 6(3), 248-253.	Frome, E. L. (1983). The Analysis of Rates Using Poisson Regression Models. Biometrics, 39(3), 665-674.
Majina mbadala	Case-Time-Control Method, Trend-Adjusted Case-Crossover, Suissa Case-Time-Control Design, Case-Crossover with Time Controls	Poisson Regression for Rates, Log-Linear Rate Model, Incidence-Rate-Ratio Regression, Poisson Regression with Offset
Zinazohusiana	4	3
Muhtasari	The case-time-control design is a pharmacoepidemiologic study design that repairs a specific weakness of the case-crossover study: bias from a secular trend in exposure. In a case-crossover analysis each case acts as their own control, comparing exposure in a short hazard window just before the event to exposure in earlier reference windows, which automatically removes all fixed, time-invariant confounders. But if the prevalence of exposure is rising or falling over calendar time for reasons unrelated to the outcome, this within-person comparison is biased. Samy Suissa's 1995 design adds a separate control series, analyzed the same way, to estimate that pure time trend; dividing the case-crossover odds ratio by the control odds ratio cancels the trend and leaves the exposure effect. Sander Greenland's 1996 analysis clarified the assumptions: the correction works only if the controls share the same exposure trend and there is no within-subject confounder, and it can introduce new bias if those conditions fail.	Poisson rate regression is the standard generalized linear model for analyzing event rates and counts, such as the number of deaths, hospitalizations, or new cases observed over a span of person-time. It models the logarithm of the expected event rate as a linear function of covariates, using a Poisson likelihood and a log link, and accommodates differing amounts of exposure by including the log of person-time as an offset. Because coefficients enter on the log scale, their exponentials are incidence-rate ratios that quantify multiplicative effects on the rate. The rate formulation was crystallized in Frome's 1983 Biometrics paper, and the model sits within the broader count-data framework developed comprehensively by Cameron and Trivedi, who also detail its central practical concern: overdispersion, where the variance exceeds the Poisson assumption and standard errors must be corrected.